Lieferketten-design und Mehrzielige Optimierung Mit Dem Bienenalgorithmus

Supply chain (SC) management has received recent growing attention by academia, industry and media. Due to the global Covid-19 pandemic, several industrial sectors have experienced disruptions at various stages in the supply chain resulting in shortages of items such as semiconductor chips for car manufacturers or shortages of commodities such as foods at the retailer stores [1]. Companies are under pressure for delivering quality products and providing services on time, at a low cost, with high service level. Therefore, they must build a robust and efficient supply chain able to support the whole life cycle of the product. A typical supply chain is composed of different stages such as multiple tiers of suppliers, manufacturing stages, distribution and wholesaler stages, retailers. Moreover, it includes the logistics operations, responsible for moving and storing the items from one stage to another. Designing a supply chain network may become very challenging because of the different factors that may affect the decision. The configuration of the supply chain will ultimately affect the cost of the finished products, the speed in reaching the final customers, as well as the service level and quality. Moreover, these factors might show opposite trends. In fact, usually the higher cost solution is also the faster, while the slower one can be performed at a lower cost. Finding the optimal configuration of a supply chain may result in a very complex optimisation problem, involving multiple objectives to satisfy. Therefore, the need of finding trade-off solutions which represent a good compromise of contrasting objectives [2]. Multi-objective optimisation problems have multiple solutions forming the so-called Pareto front. These solutions are non-dominated, meaning there is no other solution improving the value of an objective without worsening at least one of the other objectives. Usually, these problems are not solvable with analytical methods, but require instead a high computational effort by means of stochastic approaches. Meta-heuristic algorithms have proven to be a valid tool for solving NP-hard problems, and the multi-objective supply chain optimisation problem is not an exception. Among the different meta-heuristic algorithms, the Bees Algorithm (BA) belongs to the category of the nature-inspired algorithms. In fact, it mimics the food foraging behaviour of the honeybees. The BA has proven to be an effective tool in solving numerous optimisation problems, both discrete and continuous, with a single objective or multi-objective in the fields of design, manufacturing, control, clustering, scheduling as well as supply chain. In fact, the supply chain network design problem can be formulated as a multi-objective optimisation problem and the Bees Algorithm has proven to be an effective tool for finding optimal configurations of the supply chain. Therefore, how to design and optimise a supply chain network considering multiple objectives with the Bees Algorithm? The aim of this chapter is to present an effective approach based on the Bees Algorithm to design a supply chain network by solving a multi-objective optimisation model consisting of minimising the total supply chain cost and lead time simultaneously. The reminder of this chapter is structured as follows: Sect. 2 defines the supply chain network design problem statement, including the mathematical formulation of the multi-objective optimisation model. The Bees Algorithm is introduced and presented in Sect. 3. Section 4 defines the Bees Algorithm approach for solving the multi-objective supply chain optimisation problem and a numerical example is discussed. Finally, conclusions and future research opportunities are outlined in Sect. 5.